Ultrasound imaging (US) is susceptible to several types of artifacts. Most artifacts appear because of transducer limitations and simplified assumptions on the wave propagation. The artifacts are sometimes used as a component that contains tissue information; however, they often lead to a misinterpretation in the clinical diagnosis. Therefore, to improve the clinical utility of ultrasound in difficult-to-image patients and settings, a number of artifact removal methods have been proposed that aim at boosting image quality. Classical optimization-based methods have severe limitations due to their limited performance and high computation requirements. Furthermore, it is difficult to obtain parameters for producing high-quality output. A quick remedy for the aforementioned issues is the deep learning approach, which offers high performance compared with the traditional methods despite the significantly reduced runtime complexity. Another big advantage is that the same parameters as those learned during the training phase can be used to process different input images. This has motivated the scientific community to design deep-neural-network-based approaches for US artifact removal tasks.

In this chapter, we provide an overview of a recent image-reconstruction method that uses a deep generative algorithm for dynamic magnetic resonance-imaging (dMRI). We begin by briefly introducing the imaging modality of dMRI, the associated image-reconstruction problem, and existing reconstruction approaches. Next, we introduce the time-dependent deep image prior (TD-DIP), which exploits the structure of convolutional neural networks (CNNs) as a regularizing prior. We show some representative results and discuss the pros and cons of this regularizing paradigm. Finally, we discuss a few potential remaining limitations.

CryoGAN uses ideas from deep generative adversarial learning to perform image reconstruction in single-particle cryo-electron microscopy (cryo-EM). In this chapter, we begin by introducing single-particle cryo-EM. We then formulate the associated image-reconstruction problem and discuss the main solutions found in the literature. Next, we describe the CryoGAN algorithm and show some representative results. Finally, we discuss what our experiences with Cryo-GAN suggest about the advantages and disadvantages of such deep generative adversarial methods in single-particle cryo-EM and beyond.

Quantitative phase imaging (QPI) refers to label-free techniques that produce images containing morphological information. In this chapter, we focus on 2D phase imaging with a holographic setup. In such a setting, the complex-valued measurements contain both intensity and phase information. The phase is related to the distribution of the refractive index of the underlying specimen. In practice, the collected phase happens to be wrapped (i.e., modulo 2π of the original phase) and one gains quantitative information on the sample only once the measurements are unwrapped. The process of phase unwrapping relies on the solution of an inverse problem, for which numerous methods exist. However, it is challenging to unwrap the phases of particularly complex or thick specimens such as organoids. Under such extreme conditions, classical methods often exhibit unwrapping errors. In this chapter we first formulate the problem of phase unwrapping and review the existing methods to solve it. Then, we present an application of a regularizing neural network to phase unwrapping, which allows us to outline the advantages of a training-free approach, i.e., a deep image prior, over classical methods or supervised learning.

In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithms and modern deep learning techniques. To provide context, we start by tracing the origin of algorithm unrolling and providing a comprehensive tutorial on how to unroll iterative algorithms into deep networks. We then extensively cover algorithm unrolling in a wide variety of biomedical imaging modalities and delve into several representative recent works in detail. Indeed, there is a rich history of iterative algorithms for biomedical image synthesis, which makes the field ripe for unrolling techniques. In addition, we put algorithm unrolling into a broad perspective, in order to understand why it is particularly effective, and discuss recent trends. Finally, we conclude the chapter by discussing open challenges and suggesting future research directions.

From unconditional synthesis to conditional synthesis, GANs have shown that they are a perfect fit for such problems thanks to their ability to learn probability distributions. For unconditional synthesis, the objective is to stochastically generate MR images of target contrast. Conditional synthesis refers to the case where the model learns nonlinear mapping to the different MR tissue contrasts without altering the physiological information. Furthermore, by merging collaborative information of multiple contrast images, missing data imputation among many different domains is also effectively solved with GANs. Although promising results are seen, development in the area is still at its early stage. Interesting research directions are proposed from prior work, including the application of more advanced methods and rigorous validation in clinical settings. In effect, MRI image synthesis techniques should be able to reduce the burden of costly MR scans, benefiting both patients and hospitals.

The development of deep learning reconstruction methods for accelerated MR acquisitions has been an ongoing area of research for the last several years. It has been repeatedly demonstrated that deep learning methods can outperform classic reconstruction approaches in terms of both quantitative image metrics like MSE to ground truth as well as qualitative reader studies where radiologists have been questioned in a subjective way. We present the basics and well-known approaches for MR image reconstruction via deep learning.

In this chapter, we review largely targeted tasks in the computed tomography (CT) literature, including low-dose CT, sparse-view CT, limited angle CT, interior CT, etc. We present deep-learning-based methods which operate as image post-processing techniques or raw-to-image mapping techniques.

This chapter provides a summary of some popular model-based deep learning methods and their extensions. Section 8.1 briefly describes classical model-based methods and their benefit as well as limitations. Section 8.2 describes how deep learning can help in overcoming some limitations of classical model-based methods. Section 8.3 discusses how to incorporate a pre-trained deep network as a regularizer using the plug-and-play approach. Section 8.4 describes end-to-end training using a model-based deep learning framework. This section also discusses some benefits and limitations of end-to-end training. Section 8.5 and 8.6 describe unsupervised model-based deep learning approaches when a clean training dataset is not available. Section 8.6 considers model mismatch issues as well as the joint design of acquisition and reconstruction frameworks.

With the present demand for high-quality image reconstruction and signal extraction from less (e.g., unfocused or parallel) transmissions that facilitate fast imaging, and the push towards compact probes, modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, naturally lies at the heart of the ultrasound image formation chain. In this chapter, we discuss why and when deep learning methods can play a compelling role in the digital beamforming pipeline, and then show how these data-driven systems can be leveraged for improved ultrasound image reconstruction.

This chapter focuses on biomedical image reconstruction methods at the intersection of MBIR and machine learning. After briefly reviewing classical MBIR methods for image reconstruction, we discuss the combination of MBIR with unsupervised learning, supervised learning, or both. Such combinations offer potential advantages for learning even with limited data.

In this chapter, we show that image-domain deep-learning-only reconstruction methods have intrinsic limitations in reconstruction accuracy and generalizability to individual patients owing to the regressive nature of the method. The combination of deep learning methods with analytic reconstruction methods or statistical IR methods offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability.